# Secure Two-Party Feature Selection

arXiv: https://arxiv.org/abs/1901.00832 · HTML: https://mlsanity.com/paper/1901.00832/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Privacy-Preserving Feature Selection with Secure Multiparty Computation](https://mlsanity.com/paper/2102.03517/) (2021)
- [Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryption](https://mlsanity.com/paper/2505.12869/) (2025)

## Abstract (arXiv)

In this work, we study how to securely evaluate the value of trading data without requiring a trusted third party. We focus on the important machine learning task of classification. This leads us to propose a provably secure four-round protocol that computes the value of the data to be traded without revealing the data to the potential acquirer. The theoretical results demonstrate a number of important properties of the proposed protocol. In particular, we prove the security of the proposed protocol in the honest-but-curious adversary model.